This section showcases Algorithm REF  in common image classification benchmarks. We compare it to state-of-the-art data augmentation methods in terms of classification accuracy. Furthermore, we discuss other advantageous properties of our method through ablations. Namely, we focus on the ability to control the effect of data augmentation by modifying the constraint level, the informativeness of dual variables, and the benefits of adapting the augmentation distribution throughout training. We follow the setup (including the transformation sets) used in recent automatic data augmentation literature [1]}. A complete list of transformations together with other hyperparameters and training settings can be found on Appendix .
